National Hypertension Taskforce of Australia: a roadmap to achieve 70% blood pressure control in Australia by 2030
Bibliographic record
Abstract
Background: Raised blood pressure is the leading preventable cause of death in Australia. One in three Australian adults (6.8 million people) have hypertension, defined as clinic or office blood pressure greater than or equal to 140/90mmHg, based on randomised population data.10 Screening campaigns found that about half of these adults (3.4 million) have not had their high blood pressure values detected and are unaware of their hypertension,11 hence are not receiving appropriate treatment. Of those who are diagnosed with hypertension, and treated in the general population, only 32% (2.2 million) are treated effectively, that is, reducing their blood pressure to less than 140/90mmHg (Box 1).10,11 Australians who visit primary care centres have somewhat different rates, where 55% of patients are treated and have their blood pressure effectively controlled.12 Goal: Increase current population blood pressure control rates (<140/90mmHg) from 32% to at least 70% by 2030.5,10 Targets and strategies: The roadmap for 2024–2030 (Box 2) is built on three pillars: (A) prevent; (B) detect; and (C) effectively treat raised blood pressure. An international modelling study recommended 80–80–80 blood pressure targets, which translates to 80% of individuals with hypertension being screened and aware of their diagnosis; 80% of those who are aware being prescribed treatment; and 80% of those on treatment having achieved blood pressure targets.13 However, because 20% remain unaware, and 20% of those aware remain untreated, and 20% of those treated not achieving target, this model would only achieve 51% blood pressure control. To achieve the Taskforce’s target of 70%, a 90–90–90 model is required for Australia, as this approach would achieve a 73% blood pressure control rate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".